Benchmarking Autonomous Agents against Temporal, Spatial, and Semantic Evasions

• URL: http://arxiv.org/abs/2605.22321v1
• Date: Thu, 21 May 2026 11:07:51 GMT
• Title: Benchmarking Autonomous Agents against Temporal, Spatial, and Semantic Evasions
• Abstract summary: We propose a novel, multi-dimensional evasion framework targeting LLM-based agent systems.<n>To quantify these threats, we construct A3S-Bench, a comprehensive benchmark comprising 2,254 real-world agent execution trajectories.<n>Our evasion framework elevates the average risk trigger rate from a 28.3% baseline to 52.6%.
• Score: 15.535628544812326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As autonomous agents (e.g., OpenClaw) increasingly operate with deep system-level privileges to execute complex tasks, they introduce severe, unmitigated security risks. Current vulnerability analyses overwhelmingly focus on single-turn, stateless behaviors, overlooking the expanded attack surface inherent in stateful, multi-turn interactions and dynamic tool invocations. In this paper, we propose a novel, multi-dimensional evasion framework targeting LLM-based agent systems. We introduce three stealthy attack vectors: (1) Temporal evasion, which fragments malicious payloads across sequential interaction turns; (2) Spatial evasion, which conceals payloads within complex external artifacts that evade standard LLM parsing mechanisms; and (3) Semantic evasion, which obscures malicious intents beneath benign contextual noise. To systematically quantify these threats, we construct A3S-Bench, a comprehensive benchmark comprising 2,254 real-world agent execution trajectories. Evaluating a standard agent framework separately integrated with 10 mainstream LLM backbones against 20 practical threat scenarios, we demonstrate that our evasion framework elevates the average risk trigger rate from a 28.3\% baseline to 52.6\%. These findings reveal systemic, architecture-level vulnerabilities in current autonomous agent systems that existing defenses fail to address, highlighting an urgent need for defense mechanisms tailored to the unique threats.

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